This panel examines how AI can identify mental health risks early, predict crises, and guide targeted interventions—while addressing the ethical, privacy, and equity challenges that come with predictive modeling in sensitive clinical settings.

Key Discussion Points:

• Early Detection and Risk Prediction: Using AI to identify individuals at risk of depression, anxiety, or suicide before clinical symptoms become severe.

• Tailoring Interventions: How AI can help clinicians prioritize resources and personalize therapy plans for patients.

• Ethical and Legal Considerations: Consent, privacy, bias, and accountability when deploying predictive mental health tools.

• Integration into Clinical Workflows: Challenges and opportunities in incorporating predictive AI without overburdening clinicians.

• Global and Cultural Considerations: How predictive tools must adapt to diverse populations and healthcare systems.